Struct RBFSampler 

pub struct RBFSampler<State = Untrained> {
    pub gamma: Float,
    pub n_components: usize,
    pub random_state: Option<u64>,
    /* private fields */
}

Random Fourier Features for RBF kernel approximation

Approximates the RBF kernel K(x,y) = exp(-gamma * ||x-y||²) using random Fourier features (Random Kitchen Sinks).

Parameters

• gamma - RBF kernel parameter (default: 1.0)
• n_components - Number of Monte Carlo samples (default: 100)
• random_state - Random seed for reproducibility

Examples

use sklears_kernel_approximation::RBFSampler;
use sklears_core::traits::{Transform, Fit, Untrained}
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]];

let rbf = RBFSampler::new(100);
let fitted_rbf = rbf.fit(&X, &()).unwrap();
let X_transformed = fitted_rbf.transform(&X).unwrap();
assert_eq!(X_transformed.shape(), &[3, 100]);

RBFSampler

Fields

gamma: Float

RBF kernel parameter

n_components: usize

Number of Monte Carlo samples

random_state: Option<u64>

Random seed

Implementations

impl RBFSampler<Untrained>

pub fn new(n_components: usize) -> Self

Create a new RBF sampler

pub fn gamma(self, gamma: Float) -> Self

Set the gamma parameter

pub fn random_state(self, seed: u64) -> Self

Set random state for reproducibility

impl RBFSampler<Trained>

pub fn random_weights(&self) -> &Array2<Float>

Get the random weights

pub fn random_offset(&self) -> &Array1<Float>

Get the random offset

Trait Implementations

impl<State: Clone> Clone for RBFSampler<State>

fn clone(&self) -> RBFSampler<State>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<State: Debug> Debug for RBFSampler<State>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Estimator for RBFSampler<Untrained>

type Config = ()

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ()> for RBFSampler<Untrained>

type Fitted = RBFSampler<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, _y: &()) -> Result<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for RBFSampler<Trained>

fn transform(&self, x: &Array2<Float>) -> Result<Array2<Float>>

Transform the input data